Tutorial 5: CPS Microdata¶

The Current Population Survey (CPS) supplements provide specialized microdata on topics like tobacco use, voting, and food security.

Goal: Analyze tobacco use patterns across states using multi-year CPS data.

Setup¶

In [1]:

import os
from cendat import CenDatHelper
from dotenv import load_dotenv

load_dotenv()
cdh = CenDatHelper(key=os.getenv("CENSUS_API_KEY"))

# Get multiple years of CPS Tobacco Use Supplement
cdh.list_products(years=[2022, 2023], patterns="/cps/tobacco")
cdh.set_products()

import os from cendat import CenDatHelper from dotenv import load_dotenv load_dotenv() cdh = CenDatHelper(key=os.getenv("CENSUS_API_KEY")) # Get multiple years of CPS Tobacco Use Supplement cdh.list_products(years=[2022, 2023], patterns="/cps/tobacco") cdh.set_products()

✅ API key loaded successfully.

✅ Product set: 'Current Population Survey: Tobacco Use Supplement (2022/cps/tobacco/sep)' (Vintage: [2022])
✅ Product set: 'Current Population Survey: Tobacco Use Supplement (2023/cps/tobacco/jan)' (Vintage: [2023])
✅ Product set: 'Current Population Survey: Tobacco Use Supplement (2023/cps/tobacco/may)' (Vintage: [2023])

Step 2: Explore and Select Variables¶

In [2]:

# See available variable groups
cdh.list_groups()

# See available variable groups cdh.list_groups()

Out[2]:

[]

In [3]:

# Select specific variables
# PEA1, PEA3: Tobacco use questions
# PWNRWGT: Person weight
cdh.set_variables(["PEA1", "PEA3", "PWNRWGT"])
cdh.set_geos("state", "desc")

# Select specific variables # PEA1, PEA3: Tobacco use questions # PWNRWGT: Person weight cdh.set_variables(["PEA1", "PEA3", "PWNRWGT"]) cdh.set_geos("state", "desc")

✅ Variables set:
  - Product: Current Population Survey: Tobacco Use Supplement (2022/cps/tobacco/sep) (Vintage: [2022])
    Variables: PEA1, PEA3, PWNRWGT
  - Product: Current Population Survey: Tobacco Use Supplement (2023/cps/tobacco/jan) (Vintage: [2023])
    Variables: PEA1, PEA3, PWNRWGT
  - Product: Current Population Survey: Tobacco Use Supplement (2023/cps/tobacco/may) (Vintage: [2023])
    Variables: PEA1, PEA3, PWNRWGT

✅ Geographies set: 'state'

Step 3: Get Data¶

In [4]:

response = cdh.get_data(within={"state": ["06", "48"]})

response = cdh.get_data(within={"state": ["06", "48"]})

✅ Parameters created for 3 geo-variable/group combinations.

✅ Data fetching complete. Stacking results.

Step 4: Analyze with Pooled Weights¶

When combining multiple survey years, divide the weights:

In [5]:

response.tabulate(
    "PEA1",
    "PEA3",
    strat_by="state",
    weight_var="PWNRWGT",
    weight_div=3  # Divide weight for pooled years
)

response.tabulate( "PEA1", "PEA3", strat_by="state", weight_var="PWNRWGT", weight_div=3 # Divide weight for pooled years )

shape: (43, 7)
┌───────┬──────┬──────┬──────────────┬──────┬──────────────┬────────┐
│ state ┆ PEA1 ┆ PEA3 ┆            n ┆  pct ┆         cumn ┆ cumpct │
╞═══════╪══════╪══════╪══════════════╪══════╪══════════════╪════════╡
│    48 ┆   -3 ┆   -3 ┆     12,151.4 ┆  0.1 ┆     12,151.4 ┆    0.1 │
│    48 ┆   -3 ┆   -2 ┆      7,862.0 ┆  0.0 ┆     20,013.4 ┆    0.1 │
│    48 ┆   -3 ┆   -1 ┆     31,283.4 ┆  0.1 ┆     51,296.9 ┆    0.2 │
│    48 ┆   -3 ┆    3 ┆      6,011.1 ┆  0.0 ┆     57,308.0 ┆    0.3 │
│    48 ┆   -2 ┆   -9 ┆      7,299.8 ┆  0.0 ┆     64,607.8 ┆    0.3 │
│    48 ┆   -2 ┆   -2 ┆      1,871.9 ┆  0.0 ┆     66,479.6 ┆    0.3 │
│    48 ┆   -2 ┆   -1 ┆     42,634.2 ┆  0.2 ┆    109,113.9 ┆    0.5 │
│    48 ┆   -2 ┆    2 ┆      2,698.4 ┆  0.0 ┆    111,812.3 ┆    0.5 │
│    48 ┆   -2 ┆    3 ┆     11,474.7 ┆  0.1 ┆    123,287.0 ┆    0.6 │
│    48 ┆   -1 ┆   -1 ┆          0.0 ┆  0.0 ┆    123,287.0 ┆    0.6 │
│    48 ┆    1 ┆   -9 ┆     12,524.6 ┆  0.1 ┆    135,811.6 ┆    0.6 │
│    48 ┆    1 ┆   -3 ┆      6,074.9 ┆  0.0 ┆    141,886.5 ┆    0.6 │
│    48 ┆    1 ┆   -2 ┆      2,451.6 ┆  0.0 ┆    144,338.1 ┆    0.6 │
│    48 ┆    1 ┆    1 ┆  1,230,269.3 ┆  5.5 ┆  1,374,607.4 ┆    6.2 │
│    48 ┆    1 ┆    2 ┆    521,851.7 ┆  2.3 ┆  1,896,459.0 ┆    8.5 │
│    48 ┆    1 ┆    3 ┆  2,993,540.7 ┆ 13.4 ┆  4,889,999.7 ┆   21.9 │
│    48 ┆    2 ┆   -9 ┆      9,233.3 ┆  0.0 ┆  4,899,233.0 ┆   21.9 │
│    48 ┆    2 ┆   -1 ┆ 11,019,066.7 ┆ 49.3 ┆ 15,918,299.7 ┆   71.2 │
│    48 ┆    2 ┆    2 ┆     63,562.7 ┆  0.3 ┆ 15,981,862.4 ┆   71.5 │
│    48 ┆    2 ┆    3 ┆  6,364,974.8 ┆ 28.5 ┆ 22,346,837.2 ┆  100.0 │
│     6 ┆   -9 ┆   -1 ┆      2,588.4 ┆  0.0 ┆      2,588.4 ┆    0.0 │
│     6 ┆   -3 ┆   -3 ┆      4,518.2 ┆  0.0 ┆      7,106.6 ┆    0.0 │
│     6 ┆   -3 ┆   -1 ┆     38,037.9 ┆  0.1 ┆     45,144.6 ┆    0.2 │
│     6 ┆   -3 ┆    3 ┆      3,679.8 ┆  0.0 ┆     48,824.4 ┆    0.2 │
│     6 ┆   -2 ┆   -9 ┆      2,126.6 ┆  0.0 ┆     50,951.0 ┆    0.2 │
│     6 ┆   -2 ┆   -3 ┆      1,726.2 ┆  0.0 ┆     52,677.2 ┆    0.2 │
│     6 ┆   -2 ┆   -2 ┆     13,550.3 ┆  0.0 ┆     66,227.5 ┆    0.2 │
│     6 ┆   -2 ┆   -1 ┆    100,758.4 ┆  0.3 ┆    166,985.9 ┆    0.6 │
│     6 ┆   -2 ┆    3 ┆     16,578.8 ┆  0.1 ┆    183,564.7 ┆    0.6 │
│     6 ┆   -1 ┆   -1 ┆          0.0 ┆  0.0 ┆    183,564.7 ┆    0.6 │
│     6 ┆    1 ┆   -9 ┆     11,301.9 ┆  0.0 ┆    194,866.6 ┆    0.6 │
│     6 ┆    1 ┆   -3 ┆     14,985.0 ┆  0.0 ┆    209,851.7 ┆    0.7 │
│     6 ┆    1 ┆   -2 ┆     14,227.3 ┆  0.0 ┆    224,078.9 ┆    0.7 │
│     6 ┆    1 ┆    1 ┆  1,054,186.4 ┆  3.5 ┆  1,278,265.3 ┆    4.3 │
│     6 ┆    1 ┆    2 ┆    576,434.2 ┆  1.9 ┆  1,854,699.5 ┆    6.2 │
│     6 ┆    1 ┆    3 ┆  3,532,156.8 ┆ 11.8 ┆  5,386,856.3 ┆   18.0 │
│     6 ┆    2 ┆   -9 ┆     18,986.8 ┆  0.1 ┆  5,405,843.1 ┆   18.0 │
│     6 ┆    2 ┆   -3 ┆      4,104.9 ┆  0.0 ┆  5,409,947.9 ┆   18.0 │
│     6 ┆    2 ┆   -2 ┆      7,485.7 ┆  0.0 ┆  5,417,433.7 ┆   18.1 │
│     6 ┆    2 ┆   -1 ┆ 16,344,706.7 ┆ 54.5 ┆ 21,762,140.4 ┆   72.5 │
│     6 ┆    2 ┆    1 ┆     13,137.2 ┆  0.0 ┆ 21,775,277.6 ┆   72.6 │
│     6 ┆    2 ┆    2 ┆     74,384.9 ┆  0.2 ┆ 21,849,662.4 ┆   72.8 │
│     6 ┆    2 ┆    3 ┆  8,153,180.9 ┆ 27.2 ┆ 30,002,843.3 ┆  100.0 │
└───────┴──────┴──────┴──────────────┴──────┴──────────────┴────────┘